Why the study?
Does a classification method using Gabor frames on surface ECG signals accurately differentiate between paroxysmal and persistent atrial fibrillation episodes?
Does a classification method using Gabor frames on surface ECG signals accurately differentiate between paroxysmal and persistent atrial fibrillation episodes?
A novel ECG-based classification method using Gabor frames provides high accuracy (89%) for early differentiation between paroxysmal and persistent atrial fibrillation, potentially guiding optimal patient selection for catheter ablation.
In this study, we propose a new classification method for early differentiation of paroxysmal and persistent atrial fibrillation episodes, i.e. those which spontaneously or with external intervention will return to sinus rhythm within 7 days of onset from the ones where the arrhythmia is sustained for more than 7 days. Today, clinicians provide patients classification once the course of the arrhythmia has been disclosed. This classification problem is dealt with in this study. We study a sparse representation of surface electrocardiogram signals by means of Gabor frames and afterwards we apply a linear discriminant analysis. Thus, we provide an early discrimination, obtaining promising performances on a heterogeneous cohort of patients in terms of pharmacological treatment and state of progression of the arrhythmia: 95% sensitivity, 82% specificity, 89% accuracy. In this manner, the proposed method can help clinicians to choose the most appropriate treatment using the electrocardiogram, which is a widely available and non-invasive technique. This early differentiation is clinically highly significant in order to choose optimal patients who may undergo catheter ablation with higher success rates.
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Ortigosa et al. (2016) studied this question.
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